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        <h2 id="1-Machine-Learning-definition（机器学习定义）"><a href="#1-Machine-Learning-definition（机器学习定义）" class="headerlink" title="1. Machine Learning definition（机器学习定义）"></a>1. Machine Learning definition（机器学习定义）</h2><ul>
<li><p>Arthur Samuel（1959年）将机器学习非正式定义为：在不直接针对问题进行编程的情况下，赋予计算机学习能力的一个研究领域。<br>创造西洋棋程序，可以和自己对战。</p>
</li>
<li><p>Tom Mitchell(1998年)提出一个更为正式关于机器学习的定义 ：对于一个计算机程序来说：给它一个任务T和一个性能测量方法P，如果在经验E的影响下，P对T的测量结果得到了改进，那么就说改程序从E中学习。</p>
<a id="more"></a>
</li>
</ul>
<p>E = the experience of playing many games of checkers</p>
<p>T = the task of playing checkers.</p>
<p>P = the probability that the program will win the next game.</p>
<h2 id="2-Supervised-Learning-监督学习"><a href="#2-Supervised-Learning-监督学习" class="headerlink" title="2. Supervised Learning (监督学习)"></a>2. Supervised Learning (监督学习)</h2><p> 它被称作监督学习是因为对于每个数据来说 我们给出了 “正确的答案”。<br>你有一些问题和他们的答案，你要做的有监督学习就是学习这些已经知道答案的问题。然后你就具备了经验了，这就是学习的成果。然后在你接受到一个新的不知道答案的问题的时候，你可以根据学习得到的经验，得出这个新问题的答案。（试想一下高考不正是这样，好的学习器就能有更强的做题能力，考好的分数，上好的大学…..）。我们有一个样本数据集，如果对于每一个单一的数据根据它的特征向量我们要去判断它的标签（算法的输出值），那么就是有监督学习。通俗的说，有监督学习就是比无监督学习多了一个可以表达这个数据特质的标签。</p>
<p>包含回归（Regression）和分类（Classfication）。</p>
<ul>
<li>举例：<ol>
<li>房价 与 面积的关系</li>
</ol>
</li>
</ul>
<h4 id="2-1-Classfication-分类任务"><a href="#2-1-Classfication-分类任务" class="headerlink" title="2.1 Classfication (分类任务)"></a>2.1 Classfication (分类任务)</h4><p>离散变量预测,设定了标准答案，复合条件，是或否。</p>
<p><img src="https://upload-images.jianshu.io/upload_images/4090284-20697a8e0a15ac5d.PNG?imageMogr2/auto-orient/strip%7CimageView2/2/w/700" alt="image"></p>
<ul>
<li><p>举例：</p>
<ol>
<li>肿瘤良性/恶性  与 肿瘤大小的关系。（0或1结构）</li>
<li>年纪 与 肿瘤大小关系。（聚合结构）</li>
<li>预测明天的气温是多少度</li>
</ol>
</li>
<li><p>Support Vector machines(支持向量机的算法):可以吧数据映射到无限维空间中。</p>
</li>
<li><p>问题：  </p>
<ol>
<li>如何把一个无限维的向量映射到计算机内存中？   </li>
<li>如何表示一个无限纬空间中的点?</li>
</ol>
</li>
</ul>
<h4 id="2-2-Regression（回归任务）"><a href="#2-2-Regression（回归任务）" class="headerlink" title="2.2 Regression（回归任务）"></a>2.2 Regression（回归任务）</h4><p>连续变量预测。</p>
<p><img src="https://upload-images.jianshu.io/upload_images/4090284-217ab4bb24b34b9e.PNG?imageMogr2/auto-orient/strip%7CimageView2/2/w/700" alt="image"></p>
<ul>
<li>举例：<ol>
<li>预测明天的气温是多少度。</li>
</ol>
</li>
</ul>
<h2 id="3-Unsupervised-Learning-无监督学习"><a href="#3-Unsupervised-Learning-无监督学习" class="headerlink" title="3. Unsupervised Learning(无监督学习)"></a>3. Unsupervised Learning(无监督学习)</h2><p> 数据聚合、分类。</p>
<p> <img src="https://upload-images.jianshu.io/upload_images/4090284-f45f90e71dc0c70e.PNG?imageMogr2/auto-orient/strip%7CimageView2/2/w/700" alt="image"></p>
<p>我们有一些问题，但是不知道答案，我们要做的无监督学习就是按照他们的性质把他们自动地分成很多组，每组的问题是具有类似性质的（比如数学问题会聚集在一组，英语问题会聚集在一组，物理……..）。</p>
<p>所有数据只有特征向量没有标签，但是可以发现这些数据呈现出聚群的结构，本质是一个相似的类型的会聚集在一起。把这些没有标签的数据分成一个一个组合，就是聚类（Clustering）。比如Google新闻，每天会搜集大量的新闻，然后把它们全部聚类，就会自动分成几十个不同的组（比如娱乐，科技，政治……），每个组内新闻都具有相似的内容结构。</p>
<ul>
<li>举例：<ol>
<li>提供一组数据，不提供任何数据的正确答案，你能否在这组数据中寻找到一些有趣的数据结构？</li>
<li>基因数据分组。</li>
<li>聚合算法处理图像，对像素数据进行聚合分组，得到类似素描的效果，等效表达。</li>
<li>图像处理，将图像分成不同区域，像素分到不同区域。创建3D模型，创造类似AR的效果。</li>
<li>计算机集群，社交网络分析，市场划分，航天数据分析。</li>
</ol>
</li>
</ul>
<h4 id="3-1-Cocktail-party-problem-鸡尾酒会问题"><a href="#3-1-Cocktail-party-problem-鸡尾酒会问题" class="headerlink" title="3.1 Cocktail party problem(鸡尾酒会问题)"></a>3.1 Cocktail party problem(鸡尾酒会问题)</h4><p>假设我们在参加一个鸡尾酒会，有很多人参加，在环境嘈杂，很多人说话的情况下，能否把感兴趣的某个人的声音单独从嘈杂的背景音中提取出来？<br>规律：不同麦克风收集到说话者声音大小不一样。</p>
<ul>
<li>算法A<br>将人声从人声中分离</li>
<li>独立组件分析<br>将人声从音乐中分离</li>
</ul>
<p>其他应用：</p>
<ul>
<li>文本处理</li>
<li>理解功能分级</li>
</ul>
<p>使用MATLAB一行代码实现以上算法。</p>
<h4 id="3-2-ICA-algorithm"><a href="#3-2-ICA-algorithm" class="headerlink" title="3.2 ICA algorithm"></a>3.2 ICA algorithm</h4><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">[W,s,v] = svd((repmat(sum(x.*x,1),size(x,1),1).*x)*x&apos;);</span><br></pre></td></tr></table></figure>
<h2 id="4-Reinforcement-Learning-强化算法学习"><a href="#4-Reinforcement-Learning-强化算法学习" class="headerlink" title="4. Reinforcement Learning(强化算法学习)"></a>4. Reinforcement Learning(强化算法学习)</h2><blockquote>
<p>基本概念是一个被称为 回报函数的概念。<br>使用在不需要进行一次决策的情形中。</p>
</blockquote>
<ul>
<li>举例<ol>
<li>使用监督学习进行癌症预测，预测一个病人肿瘤是否为恶性。你的预测决定了病人是生死。通过决策产生一个结论，要么对，要么错。</li>
</ol>
</li>
</ul>
<p>在强化学习问题中，通常会一段时间内做出一系列的决策</p>
<ul>
<li>举例<ol>
<li>自动直升机：给你自动直升机的钥匙，能否写个程序让它飞起来？如果做了一个坏的决策，飞机可能不会马上摔下来，只有你连续做出很多坏的决策时，飞机才会摔下来。相反的，只要连续的做出正确的决策，飞机就可以飞起来了。</li>
<li>训狗，正确奖励；错误惩罚。</li>
<li>机器人领域。</li>
</ol>
</li>
</ul>

      
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  <p><span>最后更新:</span>2018年11月27日 22:11:40</p>
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          console.log("Error: " + error.code + " " + error.message);
        });
    }

    function addCount(Counter) {
      var $visitors = $(".leancloud_visitors");
      var url = $visitors.attr('id').trim();
      var title = $visitors.attr('data-flag-title').trim();
      var query = new AV.Query(Counter);

      query.equalTo("url", url);
      query.find({
        success: function(results) {
          if (results.length > 0) {
            var counter = results[0];
            counter.fetchWhenSave(true);
            counter.increment("time");
            counter.save(null, {
              success: function(counter) {
                var $element = $(document.getElementById(url));
                $element.find('.leancloud-visitors-count').text(counter.get('time'));
              },
              error: function(counter, error) {
                console.log('Failed to save Visitor num, with error message: ' + error.message);
              }
            });
          } else {
            var newcounter = new Counter();
            /* Set ACL */
            var acl = new AV.ACL();
            acl.setPublicReadAccess(true);
            acl.setPublicWriteAccess(true);
            newcounter.setACL(acl);
            /* End Set ACL */
            newcounter.set("title", title);
            newcounter.set("url", url);
            newcounter.set("time", 1);
            newcounter.save(null, {
              success: function(newcounter) {
                var $element = $(document.getElementById(url));
                $element.find('.leancloud-visitors-count').text(newcounter.get('time'));
              },
              error: function(newcounter, error) {
                console.log('Failed to create');
              }
            });
          }
        },
        error: function(error) {
          console.log('Error:' + error.code + " " + error.message);
        }
      });
    }

    $(function() {
      var Counter = AV.Object.extend("Counter");
      if ($('.leancloud_visitors').length == 1) {
        addCount(Counter);
      } else if ($('.post-title-link').length > 1) {
        showTime(Counter);
      }
    });
  </script>



  

  

  
  

  

  

  

</body>
</html>
